Artificial intelligence (AI) adoption continues to grow globally, but a new report from Hitachi Vantara reveals that organisations face significant challenges in managing data infrastructure effectively.

The 2024 global study, based on insights from 1,200 IT decision-makers across 15 countries, highlights concerns about data quality, security, sustainability, and the complexities of hybrid environments. While IT leaders acknowledge the importance of high-quality data in driving AI success, many are failing to prioritise it in practice.

The report indicates that AI adoption varies significantly across regions. Singapore stands out as a leader, with 57 per cent of large organisations having integrated the tech into their operations. This high adoption rate suggests that Singaporean businesses are better positioned to leverage their data for AI-driven insights and automation.

By contrast, Indonesia exhibits much slower adoption, with only 26 per cent of large organisations having implemented AI solutions.

Other markets with comparatively low adoption rates include the US (32 per cent), the UK (27 per cent), and Brazil (27 per cent).

The disparity between Singapore and Indonesia suggests that readiness depends on various factors, including regulatory clarity, available resources, and organisational priorities.

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Barriers to AI adoption

The report does not pinpoint a single reason for Indonesia’s slow AI adoption but provides valuable context.

A lack of clear regulatory guidance on AI has led many IT leaders to deprioritise sustainable AI practices. More than a third (34 per cent) of respondents cited the absence of industry standards as a significant barrier to implementing responsible AI initiatives. Limited resources also play a crucial role in slowing adoption.

Key concerns for IT leaders include:

Data quality (37 per cent)
Inconsistent or incomplete data undermines AI model performance.

Skilled workforce (31 per cent)
A shortage of AI and data science talent hinders progress.

Data storage (31 per cent)
Organisations struggle to manage vast amounts of AI-generated data.

Processing power (28 per cent)
Computing constraints limit scalability.

Additionally, speed and cost are prioritised over return on investment (ROI) as organisations focus on iterating AI implementations quickly. This cautious approach may further contribute to slower adoption rates in some regions.

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In Malaysia, processing power emerges as a key concern. Nearly half (43 per cent) of IT leaders in Malaysia emphasise the need for optimised software and applications that can run efficiently on existing infrastructure.

The path forward

As AI adoption accelerates, businesses must address foundational challenges to maximise its benefits.

The Hitachi Vantara report underscores the importance of prioritising data quality, ensuring regulatory clarity, and investing in infrastructure improvements. By doing so, organisations can build a more resilient ecosystem that is both scalable and sustainable.

For markets like Indonesia, where adoption lags, addressing regulatory uncertainties and workforce development could be key enablers for AI growth.

Meanwhile, in leading markets like Singapore, the focus may shift towards optimising applications and enhancing data governance practices.

As businesses worldwide navigate the evolving AI landscape, the ability to manage data infrastructure effectively will remain a decisive factor in long-term success.

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